Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Machine Learning Model of Gyro Sensor Data for Drone Flight Control

드론 비행 조종을 위한 자이로센서 데이터 기계학습 모델

  • Ha, Hyunsoo (Department of Computer Science and Engineering, The Catholic University of Korea) ;
  • Hwang, Byung-Yeon (Department of Computer Science and Engineering, The Catholic University of Korea)
  • Received : 2017.01.25
  • Accepted : 2017.05.20
  • Published : 2017.06.30
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Abstract

As the technology of drone develops, the use of drone is increasing, In addition, the types of sensors that are inside of smart phones are becoming various and the accuracy is enhancing day by day. Various of researches are being progressed. Therefore, we need to control drone by using smart phone's sensors. In this paper, we propose the most suitable machine learning model that matches the gyro sensor data with drone's moving. First, we classified drone by it's moving of the gyro sensor value of 4 and 8 degree of freedom. After that, we made it to study machine learning. For the method of machine learning, we applied the One-Rule, Neural Network, Decision Tree, and Navie Bayesian. According to the result of experiment that we designated the value from gyro sensor as the attribute, we had the 97.3 percent of highest accuracy that came out from Naive Bayesian method using 2 attributes in 4 degree of freedom. On and the same, in 8 degree of freedom, Naive Bayesian method using 2 attributes showed the highest accuracy of 93.1 percent.

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References

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